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Posted to issues@spark.apache.org by "Karen Feng (Jira)" <ji...@apache.org> on 2022/06/03 18:24:00 UTC

[jira] [Updated] (SPARK-39376) Do not output duplicated columns in star expansion of subquery alias of NATURAL/USING JOIN

     [ https://issues.apache.org/jira/browse/SPARK-39376?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Karen Feng updated SPARK-39376:
-------------------------------
    Description: 
A bug was introduced in https://issues.apache.org/jira/browse/SPARK-34527 such that the duplicated columns within a NATURAL/USING JOIN were output from the qualified star of a subquery alias. For example:

{code:java}
val df1 = Seq((3, 8)).toDF("a", "b") 
val df2 = Seq((8, 7)).toDF("b", "d") 
val joinDF = df1.join(df2, "b")
joinDF.alias("r").select("r.*")
{code}

Outputs two duplicate `b` columns, instead of just one.

  was:
A bug was introduced in https://issues.apache.org/jira/browse/SPARK-34527 such that the duplicated columns within a NATURAL/USING JOIN were output from the qualified star of a subquery alias. For example:

```
val df1 = Seq((3, 8)).toDF("a", "b") 
val df2 = Seq((8, 7)).toDF("b", "d") 
val joinDF = df1.join(df2, "b")
joinDF.alias("r").select("r.*")
```

Output two duplicate `b` columns.


> Do not output duplicated columns in star expansion of subquery alias of NATURAL/USING JOIN
> ------------------------------------------------------------------------------------------
>
>                 Key: SPARK-39376
>                 URL: https://issues.apache.org/jira/browse/SPARK-39376
>             Project: Spark
>          Issue Type: Bug
>          Components: SQL
>    Affects Versions: 3.2.0
>            Reporter: Karen Feng
>            Priority: Major
>
> A bug was introduced in https://issues.apache.org/jira/browse/SPARK-34527 such that the duplicated columns within a NATURAL/USING JOIN were output from the qualified star of a subquery alias. For example:
> {code:java}
> val df1 = Seq((3, 8)).toDF("a", "b") 
> val df2 = Seq((8, 7)).toDF("b", "d") 
> val joinDF = df1.join(df2, "b")
> joinDF.alias("r").select("r.*")
> {code}
> Outputs two duplicate `b` columns, instead of just one.



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